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DEPARTMENT OF ECONOMICS

ISSN 1441-5429

DISCUSSION PAPER 07/13

Are Shocks to Disaggregated Energy Consumption in Malaysia Permanent or

Temporary? Evidence from LM Unit Root Tests with Structural

Hooi Hooi Lean and Russell Smyth

Abstract The objective of this paper is to examine whether energy consumption in Malaysia, disaggregated

by sector and type, is stationary or contains a unit root. To realize our objective we apply the

Lagrange multiplier (LM) family of unit root tests with up to two structural breaks. Depending on

the decision rule for selecting between results in the no-break, one break and two-break cases, we

find that energy consumption is stationary for between 50 per cent and 70 per cent of the

disaggregated energy series and between 25 per cent and 50 per cent of sectors. Implications for the

Malaysian government’s attempts to reduce fossil fuel consumption are discussed.

Hooi Hooi Lean Economics Program, School of Social Sciences Universiti Sains Malaysia, Malaysia

Email: hooilean@usm.my

Russell Smyth Department of Economics Monash University, Australia Email: russell.smyth@monash.edu

© 2013 Hooi Hooi Lean and Russell Smyth

All rights reserved. No part of this paper may be reproduced in any form, or stored in a retrieval system, without the prior written

permission of the author.

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Introduction

Beginning with Narayan and Smyth [1] a sizeable literature has developed around

testing for a unit root in energy consumption. A recent survey article identified 32

related studies that have been published since 2007 [2]. That survey article also

pointed to gaps in the extant literature on testing for a unit root in energy consumption

or production and offered suggestions for future research. Among the major gaps

identified in the survey article was the lack of single country studies for countries

other than the United States, (a) which consider the integration properties of

disaggregated energy consumption; and (b) consider whether the integration

properties of energy consumption vary according to sector.

The need for further detailed studies of the integration properties of disaggregated

energy consumption is particularly true for developing countries. The International

Energy Agency predicts that there will be a 53 per cent increase in global energy

consumption by 2030 and that 70 per cent of the increase in energy consumption will

occur in the developing world [3]. However, the relative intensities with which

different countries, and sectors within specific countries, consume energy will vary

according to energy type. Aggregate energy consumption does not give an indication

of the relative intensities with which different energy types are employed or how

energy consumption varies across sectors [4]. In terms of testing for a unit root in

energy consumption, it is important to take account of heterogeneity in energy

consumption because some forms of energy are more volatile than others and some

end consumers are larger than others. If energy consumption is stationary, shocks to

energy consumption will have temporary effects, but if energy consumption contains

a unit root, shocks to energy consumption will be permanent [1, 2]. This, in turn, has

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implications for the efficacy of policies designed to curtail fossil fuel consumption. If

fossil fuel consumption contains a unit root, policies designed to curtail consumption

will be effective because such policies will push consumption below its long-run

trend path in the absence of such policies [2]. The extent of consumption in a sector

and volatility in consumption will influence the extent to which shocks to energy

consumption will result in deviations from the long-run equilibrium, the level of

persistence following a shock and, thus, whether there is a unit root [5].

The need for further detailed single country studies is reinforced by the fact that there

is no clear consensus about the integration properties of various types of

disaggregated energy [5-20], nor the sectors in which energy is likely to be stationary

[17, 21, 22]. This paper responds to the call for further detailed single country studies

which consider the integration properties of disaggregated energy and whether the

integration properties differ by sector by exploring this issue for Malaysia.

A feature of the study is that we use final energy demand, rather than final energy

consumption. Final energy demand is final energy consumption plus transmission and

distribution (T&D) losses [23]. We consider final energy demand a better measure

than energy consumption. While T&D losses can be sizeable, apart from technical

losses, all energy generated contributes to economic growth [24]. Electric power T&D

losses include losses in transmission between sources of supply and points of

distribution to consumers including meter tampering, meter malfunction, billing

irregularities, illegal connections and unpaid bills [25]. In most of the ASEAN

countries, including Malaysia, electro-mechanical induction electricity meters, which

are readily susceptible to tampering, are employed [25]. However, the most common

form of non-technical losses occurs via illegal direct connection to power lines,

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especially by street vendors and in shanty towns [25]. Since 1970 annual electric

power T&D losses as a percentage of output have varied from a low of 1.3 per cent to

12.1 per cent. In several years; namely, 1974, 1976, 1993 and 1996, the T&D losses

have exceeded 10 per cent [26]. In the 1990s power theft in Malaysia was responsible

for annual losses of RM500 million ($US 165 million) [27]. More recently, in 2004,

Malaysia’s largest electricity supplier – Tenaga Nasional Berhad (TNB) – recorded

losses of RM800 ($US265 million) due to non-technical losses [25].

The Malaysian Context

Since the mid-1980s Malaysia has enjoyed high rates of economic growth

accompanied by high-energy consumption. In 1985 the Malaysian government

implemented the Industrialization Plan, which has resulted in a structural shift from

an agricultural-based to a manufacturing and service-based economy [28]. The

industrial sector now accounts for 48.1 per cent of Gross Domestic Product (GDP),

the service sector accounts for 43.6 per cent and agriculture accounts for 8.3 per cent

[29]. Over the last two decades, growth in Malaysia’s GDP has averaged 6 per cent

[30]. Between 1990 and 2005 total primary energy consumption increased from 19.6

million tons of oil equivalent (Mtoe) in 1990 to 60.4 Mtoe in 2005 [31].

However, to say that high economic growth has been accompanied by an increase in

energy consumption masks significant changes in the energy mix over time. The

major energy source in Malaysia at the beginning of the 1980s was oil. In 1981 the

Malaysian government introduced the four-fuel diversification strategy, which has

seen consumption of oil fall with a commensurate increase in consumption of natural

gas. Consumption of coal has also increased, primarily as a result of moves by the

Malaysian government to reduce the dependence of power generation on natural gas

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[30]. Table 1 shows the change in the energy mix in Malaysia since 1980. In 1980,

oil/diesel accounted for 87.9 per cent of energy consumption, while coal and natural

gas accounted for just 0.5 per cent and 7.5 per cent respectively. By 2010,

consumption of oil/diesel had dropped to a miniscule 0.2 per cent of total energy

consumption, while consumption of natural gas (55.9 per cent) and coal (36.5 per

cent) together accounted for over 90 per cent of total energy consumption. Electricity

is largely dependent on fossil-fuels. About 95 per cent of electricity generation comes

from fossil fuels with coal alone responsible for about 30 per cent in 2009 [38].

There is also considerable variation in energy consumption by sector. Table 2 shows

variation in primary energy demand by sector since 1980. The transport sector is the

largest consumer of energy at just over 40 per cent of total consumption. The

industrial sector is the second largest consumer of energy at just under 40 per cent of

total consumption. By comparison, residential and commercial (around 13 per cent)

and non-energy consumption (6-7 per cent) has been relatively low.

Over the next two decades, energy demand is expected to grow at 5 per cent per

annum [33]. Most of this growth is expected to be due to high demand in the

manufacturing and transport sectors. The transport sector is expected to be a major

driver as continued increases in living standards generate further increases in car

ownership [34]. The industrial sector will be an important consumer of energy

because Malaysia’s Vision 2020 puts it as Malaysia’s main vehicle for promoting

economic growth [34]. In terms of energy type, over the next two decades gas is

projected to increase 6.3 per cent, electricity 5.3 per cent, oil 4.7 per cent, coal 2.8 per

cent and other fuels 1.5 per cent [35]. To the extent that Malaysia becomes over-

reliant on coal or natural gas, it is expected to build more coal-fired plants [33].

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Malaysia’s reliance on fossil fuel consumption has raised serious concerns about its

effect on the environment. Malaysia is among the largest emitters of greenhouse gas

emissions in the world. Between 1990 and 2006 Malaysia’s compounded average

growth rate in greenhouse gas emissions was 7.9 per cent [36]. Between 2005 and

2020, Malaysia’s greenhouse gas emissions are expected to increase 74 per cent from

189 million tonnes of CO2e to 382 million tonnes of CO2e [36]. At the Copenhagen

Climate Change Conference in 2009, Malaysia conditionally agreed to reduce

greenhouse gas emissions as a proportion of GDP by up to 40 per cent by 2020

compared with 2005 levels. The Ninth Malaysian Plan (2006-2010) focused attention

on the sustainable development of the energy sector and a move away from fossil

fuels. In 2009, the Malaysian government introduced a National Green Technology

Policy. A number of policies have been implemented to reduce reliance on fossil

fuels. While biomass and hydro constitute a relatively small part of Malaysia’s energy

mix (see Table 1), several of the policies designed to reduce fossil fuel consumption

in the energy mix are being discussed and/or implemented. Many of these policies are

discussed in Refs. [3, 29-30, 33-39]. Whether these policies will be effective will

depend on whether the various energy types are stationary. To be effective in

reducing fossil fuel consumption, a negative shock to the long-run growth path needs

to have a permanent effect on consumption (i.e. there needs to be a unit root).

Data and Methodology

Our data is final energy demand by fuel type and final energy demand by sector, both

measured in ktoe. The sample and time period is annual data from 1978 to 2010.

Figures 1 and 2 present time series plots for all the energy variables employed in the

study. All data are sourced from the Malaysia Energy Information Hub [40].

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To test for a unit root we implement the Lagrange multiplier (LM) unit root test with

zero, one and two structural breaks proposed by Schmidt and Phillips [41] and Lee

and Strazicich [42]. We allow for one break in the intercept (Model A) and intercept

and trend (Model C) and then proceed to allow for two breaks in the intercept (Model

AA) and intercept and trend (Model CC). One advantage of employing the LM family

of tests is that it has been shown to have better properties than the one and two-break

ADF-type test proposed by Zivot and Andrews [43] and Lumsdaine and Papell [44]

(see [42]). A second advantage of using the LM family of tests is that the one and

two-break case have been frequently used in the literature (see [8, 11, 19, 21-22, 45-

49). Thus it assists in comparing results with findings from previous studies. We do

not reproduce the detailed equations behind the LM family of unit root tests, given the

tests have been widely used and are well known. We refer the interested reader either

to the original papers [41, 42] or the previous studies to have employed the tests.

Results

Table 3 contains the findings for the two Schmidt and Phillips [41] test statistics;

namely, Z(ρ) and Z(τ). We fail to reject the null hypothesis of a unit root for each

energy type as well as each of the four sectors considered at all lags. Thus, the

Schmidt and Phillips [41] LM unit root results unanimously suggest that shocks to

disaggregated energy by type and sector will have permanent effects on consumption.

The limitation of the Schmidt and Phillips [41] LM unit root is that it does not

account for potential structural break(s) in the series. The presence of structural

break(s) in the time series reduces the power to reject the unit root null [50]. To

address this issue, we first allow for one break in the LM unit root test. Table 4

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presents the results for Lee and Strazicich’s [25] Model A, which allows for one break

in the intercept. The results for Lee and Strazicich’s [25] Model C, which

accommodates one break in the intercept and slope, are presented in Table 5.

Beginning with Table 4, there is slightly more evidence of stationary for

disaggregated energy in that the LM test with one break in the intercept rejects the

unit root null for electricity and LPG at the 5 per cent level. However, the results

presented in Table 4 are consistent with those in Table 3 in suggesting that energy

demand in each of the sectors is non-stationary. Turning to the results in Table 5,

there is more evidence of stationarity by energy type and sector. The LM unit root test

with one break in the intercept and slope rejects the unit root null for seven of the ten

energy types at 5 per cent or better (diesel, motor petrol, LPG, kerosene, natural gas,

coal and coke and electricity) and a further energy type (ATF and AV gas) at the 10

per cent level. Model C also rejects the unit root null for three of the four sectors at

the 5 per cent level or better (industrial, non-energy and transport) and the fourth

sector (residential and commercial) at the 10 per cent level.

A natural question to ask is that given the findings from Models A and C and the no-

break differ, which is to be preferred? Evidence from Monte Carlo simulations

suggests that Model C has better properties than Model A [51, 52]. Model C also has

the advantage over Model A that it is more general in that it allows for a potential

structural break in the slope. Thus, between Models A and C, the findings from Model

C are to be preferred. When comparing the results for Model C with the no-break

case, it is to be remembered that allowing for a structural break will not necessarily

result in more rejections because the absolute value of the critical values also

increases [53]. A rule of thumb employed in previous studies is that in cases in which

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the no-break and one-break Model C cases differ, the results of Model C should be

preferred if the break in the intercept and slope is significant (see eg. [54, 55]). Of the

disaggregated energy series for which Model C rejects the unit root null, this is the

case for motor petrol, LPG, coal and coke and electricity. Of the four sectors this is

the case for non-energy. Hence, on the basis of a comparison of the no-break case,

Model A and Model C, we conclude that the unit root null is rejected for four

disaggregated energy series (motor petrol, LPG, coal and coke and electricity) and

only one sector; namely, non-energy. Even after allowing for one break there is still

considerable evidence that shocks to energy consumption will have permanent effects.

Thus far, we have accommodated one structural break in the time series. We have

found evidence of stationarity in 40 per cent of the energy series and 25 per cent of

sectors at the 5 per cent level or better. It may be that in allowing for a second

structural break, we can find evidence of stationarity in the remaining series at the 5

per cent level or better. To this end, we report the LM unit root with two breaks in the

intercept (Model AA) in Table 6 and the LM unit root test with two breaks in the

intercept and slope (Model CC) in Table 7. We begin with the results for Model AA

in Table 6. There is little further evidence of stationarity compared with the findings

for Model A in Table 4. The unit root null continues to be rejected for electricity and

LPG, but this time only at 10 per cent. The only sector for which the unit root null is

rejected is commercial and residential and again only at the 10 per cent level.

We now turn to the results for Model CC presented in Table 7. The unit root null is

rejected for 70 per cent of the disaggregated energy series at the 5 per cent level or

better (diesel, fuel oil, LPG, ATF and AV gas, natural gas, coal and coke and

electricity). However, the unit root null is only rejected for two of the four sectors

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(non-energy and residential and commercial) and only at the 10 per cent level.

How do we decide which of these alternative sets of findings is more reliable? First,

we consider Model CC versus Model AA, given that Model CC provides more

evidence of stationarity. In contrast to the one break case, there is no Monte Carlo

evidence on which model is preferable in a statistical sense. However, as in Model A

versus Model C, it remains that Model CC is more general in that it allows for two

potential breaks in the slope. Hence, we prefer Model CC over Model AA. This is

consistent with the approach taken in previous studies (see eg. [19]).

Next we consider Model CC versus Model C and the no-break case. The no-break

case fails to reject the unit root null for any energy series or sector. While both Model

C and CC suggest that the unit root null is rejected for 70 per cent of the

disaggregated energy series at 5 per cent or better, the models differ across some

specific energy types. To be specific, the unit root null hypothesis is rejected for fuel

oil in Model CC, but not Model C, while the unit root null is rejected for motor petrol

and kerosene in Model C, but not Model CC. Finally, while both Models C and CC

reject the unit root null in ATF and AV gas, in Model CC it is rejected at 5 per cent

and in Model C it is rejected at 10 per cent. Thus, at the 5 per cent level or better,

Models C and CC differ on four series (fuel oil, motor petrol, kerosene and ATF and

AV gas). The results for Models C and CC also differ for 75 per cent of the sectors

(industrial, transport and non-energy) at the 5 per cent level or better.

As discussed above, we prefer Model C to the no-break case if the break in the

intercept and slope are significant. We prefer Model CC to the hybrid results based

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on this selection rule if the second break in the intercept and slope are significant.

This again follows the rule of thumb employed in previous studies (see eg. [54, 55]).

Using this selection rule, we summarize a set of preferred results across the no-break

case, Model C and Model CC in the second column of Table 8 (titled ‘rule of thumb

requiring break in the intercept and slope’). Overall, we reject the unit root null for

50 per cent of the disaggregated energy series (motor petrol, LPG, ATF and AV gas,

coal and coke and electricity) and for one sector (non-energy).

One might argue that a rule of thumb that requires both the break in the intercept and

slope to be significant is too strict. Thus, we also compare the no-break case, one

break and two break cases only requiring the break in the intercept or slope to be

significant. Thus, comparing Model C with the no-break case, with this rule of thumb

we prefer the results of Model C if the break in the intercept or slope is significant.

Comparing Models CC and C, the rule of thumb is we prefer Model CC if the second

break in the intercept or slope is significant. The results are reported in the second

column of Table 8 (titled ‘rule of thumb requiring break in the intercept or slope’). As

one would expect, there is slightly more evidence of stationarity. We now reject the

unit root null for 70 per cent of the disaggregated energy series (the same five with

the stricter rule plus fuel oil and natural gas). We reject the unit root null for two of

the four sectors (non-energy and residential and commercial), but only at 10 per cent.

What explains the different results across disaggregated energy types? One potential

explanation is that those energy types of which a country has abundant resources are

more likely to be stationary [56]. The reasoning is that if a country is rich in resources

of a particular energy type it can maintain stability in consumption [57]. Malaysia is

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relatively rich in reserves of oil, mostly off Peninsular Malaysia. In 2012, it was

ranked 24th

in terms of world oil reserves [40]. In January 2012 Malaysia had oil

reserves of 4 billion barrels, down from a peak of 4.6 billion barrels in 1996 [32, 58].

Malaysia is also rich is natural gas, primarily in East Malaysia. Malaysia was ranked

12th

in terms of world natural gas reserves in 2012 [32]. In January 2011, she had 83

trillion cubic feet of natural gas reserves [32]. In contrast, although there are some

coal reserves in Malaysia, only a small fraction are being mined. While Malaysia is a

net exporter of natural gas and oil, it is a net importer of coal [32, 58].1 Our results

only provide, at best, mixed evidence that stationarity of disaggregated energy is

related to reserves. Some crude oil distillates are stationary, but others are not.

Natural gas is only stationary if one adopts the more relaxed rule of thumb for

comparing the no-break case with Models C and CC. Meanwhile, coal and

electricity, which is fueled by coal powered plants, were found to be stationary.

A second possible explanation is that stationarity is related to volatility in the series.

The argument is that series which are more volatile will be more likely to exhibit a

unit root because shocks, which generate volatility in the first place, will be more

persistent [5-7, 59]. There is little support for this hypothesis. The most volatile fuel

types are coal and coke, fuel oil and non-energy. Coal and coke is stationary, non-

energy contains a unit root and for natural gas the conclusion depends on the rule of

thumb employed. Among the sectors, the most volatile is non-energy. And non-

energy is the one sector for which the unit root null is clearly rejected.

A third possible explanation is that stationarity is related to average consumption. The

1 Malaysia’s reserves of oil have been declining since 2005. Malaysia may move from being a net

exporter of oil to a net importer of oil in 2013-2015 [32, 38], but this is outside the range of this study.

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hypothesis is that disaggregated energy types in which average consumption is high,

and in sectors with high average consumption, there is more likely to be a unit root

because shocks to consumption will result in larger deviations from the long-run trend

path [56]. There is no real support for this hypothesis when it comes to disaggregated

energy types. Some energy types with high-and-mid level consumption (electricity,

motor petrol, LPG, ATF and AV gases and coal) are stationary, while energy types

with low average consumption (kerosene and non-energy) contain a unit root. There

is, however, more support for this hypothesis when it comes to sectors. The biggest

energy consumers (industrial and transport) are clearly non-stationary, while for the

smaller consumers (non-energy and possibly residential and commercial depending

on the rule of thumb employed) the unit root null hypothesis is rejected.

Overall, we conclude that none of these explanations can conclusively rationalize

which series are non-stationary on their own. The most promising explanation seems

to be that the stationarity of energy consumption by sector is related to its average

consumption. This is similar to previous findings for the United States [5]. We agree

with Hasanov and Telatar when they suggest that a combination of the above factors,

or a subset of the above factors, is likely to explain which series have a unit root [57].

In considering the break dates we focus on our preferred models (Models C, CC).

Most of the breaks occur in the latter half of the 1980s and first half of the 1990s or

the latter half of the 1990s and first half of the 2000s. Several of the structural breaks

are broadly linked to periods of recessions and economic recovery. Periods of

recession are associated with a slowdown in energy use and periods of recovery with

a commensurate pick-up. Of relevance here is the global recession in the late 1980s,

14

following the Wall Street stock market collapse; the global recession in the early

1990s; the Asian financial crisis and the global recession in the early 2000s.

The structural breaks in the first half of the 2000s are also likely to be due to Malaysia

strengthening its commitment to renewable energy at the expense of fossil fuel

consumption in this period. Examples are introduction of the Small Renewable

Energy Program and Malaysia becoming a signatory of the Kyoto protocol in 2002.

There are no breaks in the first half of the 1980s, associated with the four-fuel

diversification strategy, and only one break after 2005, associated with decline in oil

reserves, the promotion of renewable energy in the Ninth Five-Year Plan and the

National Green Technology Policy as well as rising prices for petroleum-based

products. This, though, likely reflects the timeframe of the study.

Conclusion

Overall, our results are consistent with the received wisdom that adding a structural

break(s) increases the power to reject the unit root null and results in finding a higher

proportion of stationary cases (see the discussion in Ref. [2]). This is certainly true if

one compares the no-break case to Model C, although our results also indicate that the

marginal returns in this respect of adding a second break are small.

Our results suggest that the unit root null can be rejected for between 50 per cent and

70 per cent of disaggregated energy types, depending on the rule of thumb for

selecting between the results for the no-break case, Model C and Model CC. The

implication is that the efficacy of government policies designed to induce negative

shocks to consumption will vary according to energy types. Based on the stricter rule

of thumb, requiring the break in intercept and slope to be significant, policies causing

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negative shocks to diesel, fuel oil, kerosene, natural gas and non-energy uses would

displace consumption from the long-run growth path and be effective in bringing

about long-term reductions. However, policies designed to generate reductions in

motor petrol, LPG, ATF and AV gas, coal and coke and electricity would also result

in short-run deviations from the long-run trend path and, thus be ineffective.

More promising in terms of suggesting policies to reduce fossil fuel consumption are

the sector results. Our findings suggest that policies designed to reduce energy

consumption in the industrial and transport sectors will have lasting effects. The

results for the residential and commercial sector are less clear. Results, based on the

strict rule of thumb, suggest that policies designed to reduce energy consumption in

the residential and commercial sector will also be effective in inducing a long-term

deviation from the trend path. The results, based on the more relaxed rule of thumb,

suggest that deviations from the long-run trend path in the commercial and residential

sector will only be temporary, but this result is only weakly significant.

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20

Table 1: Energy Mix in Malaysia

Source 1980 (%) 1990 (%) 2000 (%) 2005 (%) 2010 (%)

Oil/diesel 87.9 71.4 4.2 2.2 0.2

Natural gas 7.5 15.7 77.0 70.2 55.9

Hydro 4.1 5.3 10.0 5.5 5.6

Coal 0.5 7.6 8.8 21.8 36.5

Biomass _ _ _ 0.3 1.8

Source – Oh et al [3].

21

Table 2: Primary Energy Demand by Sector

Source 2000 (%) 2005 (%) 2010 (%) Average Annual Growth

Rate

8MP 9MP

Transport 40.6 40.5 41.1 5.5 6.6

Industrial (a) 38.4 38.6 38.8 5.7 6.4

Residential and

Commercial

13.0 13.1 12.8 5.6 6.0

Non-energy(b) 7.6 7.3 6.5 4.7 4.0

Agriculture

and Forestry

0.4 0.5 0.8 12.9 15.9

Total (c) 1243.7 1631.7 2217.9 5.6 6.3

Per capita

consumption

52.9 62.2 76.5 3.3 4.2

Notes:

(a) includes manufacturing, construction and mining

(b) Includes natural gas, asphalt, bitumen, industrial feedstock and grease,

lubricants

(c) Total demand in petajoules

Source: Rahmin & Liwan [32]

22

Table 3: Results of LM Test with No Break (Schmidt and Phillips [41])

Lag 0 1 2 3 4

Z(ρ) Z(τ) Z(ρ) Z(τ) Z(ρ) Z(τ) Z(ρ) Z(τ) Z(ρ) Z(τ)

Energy Demand by Fuel Type

Diesel -5.3889 -1.6867 -5.7294 -1.7392 -5.8828 -1.7623 -5.8191 -1.7528 -5.8169 -1.7525

Fuel Oil -3.1849 -1.2738 -3.7597 -1.3839 -4.1447 -1.4531 -4.2443 -1.4704 -4.2807 -1.4767

Motor Petrol -4.2513 -1.4843 -4.2366 -1.4817 -4.8073 -1.5784 -5.6853 -1.7165 -6.3889 -1.8196

LPG -7.0435 -1.9553 -5.0207 -1.6508 -5.9010 -1.7897 -6.4030 -1.8642 -6.9724 -1.9454

Kerosene -6.0549 -1.7978 -7.9319 -2.0577 -8.2150 -2.0941 -8.1286 -2.0831 -7.9392 -2.0587

ATF and

AV Gas

-6.5888 -1.8838 -5.0855 -1.6550 -5.6453 -1.7437 -5.6104 -1.7383 -6.2719 -1.8380

Non-Energy -12.1522 -2.6873 -13.5303 -2.8356 -13.5393 -2.8365 -13.6413 -2.8472 -13.4244 -2.8245

Natural Gas -3.4576 -1.3301 -5.4024 -1.6626 -6.1656 -1.7761 -6.2069 -1.7821 -5.9765 -1.7487

Coal and

Coke

-6.5632 -1.8798 -4.9973 -1.6403 -6.2475 -1.8340 -6.7325 -1.9039 -7.3900 -1.9947

Electricity -1.2829 -0.7964 -2.0229 -1.0001 -2.6998 -1.1554 -3.3592 -1.2888 -3.8477 -1.3793

Energy Demand by Sectors

Industrial -3.8594 -1.4098 -5.2309 -1.6413 -5.7564 -1.7217 -5.7295 -1.7177 -5.7674 -1.7234

Transport -3.2927 -1.2963 -3.5206 -1.3404 -3.8740 -1.4060 -4.4547 -1.5077 -5.0722 -1.6088

Non-Energy -8.2122 -2.1325 -7.2793 -2.0077 -8.5370 -2.1743 -8.4562 -2.1639 -8.1057 -2.1186

Residential

and

Commercial

-8.0864

-2.1138 -7.1356 -1.9856 -7.2500 -2.0015 -7.8902 -2.0880 -8.3448 -2.1473

Note: critical value determination is based on Schmidt and Phillips [41, Tables 1A & 1B].

23

Table 4: Results of LM Test with One Break (Lee and Strazicich, [42]) - Model A

Series TB K St-1 Bt

Energy Demand by Fuel Type Diesel 1988 0 -0.1788

(-1.7725)

0.0951

(0.3435) Fuel Oil 2001 1 -0.1139

(-1.4998)

0.2343

(0.9034) Motor Petrol 1999 4 -0.2883

(-2.4940)

-0.1048**

(-2.3471) LPG 1998 3 -0.4835

**

(-3.6469)

0.9634***

(3.0782) Kerosene 1997 1 -0.2538

(-2.2835)

0.1855

(1.0963) ATF and AV Gas 1985 2 -0.2362

(-2.4042)

0.2894**

(2.6819) Non-Energy 1991 0 -0.4519

(-3.0565)

0.2290

(1.4231) Natural Gas 1984 2 -0.3305

(-3.0164)

1.0327***

(3.2557) Coal and Coke 1984 0 -0.2354

(-2.0663)

0.3698

(1.1067) Electricity 2000 4 -0.1482

**

(-4.0162)

0.0774***

(2.8116) Energy Demand by Sectors

Industrial 1992 4 -0.1859

(-2.0315)

-0.0877

(-0.8850) Transport 1992 4 -0.1495

(-1.7065)

-0.0505

(-1.1253) Non-Energy 1997 2 -0.3938

(-2.5487)

0.1420

(0.5700) Residential and Commercial 1995 0 -0.4857

(-3.2036)

0.1981***

(5.6382)

Notes: The test statistic is St-1 , Bt is the break point in the intercept, k is the lag length and TB is the

break date. The t-values are presented in parenthesis. Critical values for the LM test at 10%, 5% and 1% significant levels = -3.211, -3.566, -4.239. * (**) *** denote statistical significance at the 10%, 5% and 1% levels respectively.

24

Table 5: Results of LM Test with One Break (Lee and Strazicich, [42]) - Model C

Series TB k St-1 Bt Dt

Energy Demand by Fuel Type

Diesel 2004 2 -1.4312***

(-6.7496)

-0.1928

(-0.9772)

-0.0592

(-0.4862)

Fuel Oil 1998 2 -0.4552

(-3.6160)

0.0935

(0.4059)

0.1367

(1.1184)

Motor Petrol 2004 4 -1.0772***

(-5.1303)

0.1010**

(2.5419)

-0.1916***

(-5.5032)

LPG 1994 1 -0.8522**

(-4.5350)

1.4038***

(7.1576)

-0.4804***

(-5.1378)

Kerosene 2001 4 -1.3917***

(-5.7357)

-0.0620

(-0.4307)

0.0485

(0.8357)

ATF and AV Gas 1996 0 -0.7136*

(-4.2133)

0.0299

(0.3342)

0.0322

(0.8325)

Non-Energy 2000 3 -0.9431

(-3.7892)

0.1156

(0.6596)

-0.2206***

(-2.8244)

Natural Gas 1988 1 -0.4309**

(-4.5973)

0.1081

(0.5036)

0.0146

(0.1407)

Coal and Coke 1985 0 -1.1056***

(-6.2892)

-0.5097**

(-2.4851)

-0.2840***

(-3.3017)

Electricity 1992 3 -0.3161***

(-5.1862)

-0.0442*

(-1.9023)

0.0344**

(2.4475)

Energy Demand by Sectors

Industrial 2000 4 -0.7485**

(-4.6022)

-0.0403

(-0.5864)

0.0657*

(1.7988)

Transport 1997 4 -0.6380**

(-4.6816)

-0.1590***

(-4.4720)

0.0309

(1.4844)

Non-Energy 1993 3 -1.4362***

(-5.3964)

-0.2965*

(-1.7810)

-0.1276**

(-2.1820)

Residential and

Commercial

1994 0 -0.7059*

(-4.1781)

0.0050

(0.1361)

0.0463**

(2.6122)

Critical values

location of break, λ 0.1 0.2 0.3 0.4 0.5

1% significant level -5.11 -5.07 -5.15 -5.05 -5.11 5% significant level -4.50 -4.47 -4.45 -4.50 -4.51

10% significant level -4.21 -4.20 -4.18 -4.18 -4.17 Notes: In addition to the notes in Table 4, Dt is the break in the slope. The critical values are symmetric around λ and (1-λ).

25

Table 6: Results of LM Test with Two Breaks (Lee and Strazicich, [42]) Model

AA

Series TB1 TB2 k St-1 Bt1 Bt2

Energy Demand by Fuel Type Diesel 1988 2007 0 -0.1955

(-1.7415)

0.0930

(0.3155)

0.0953

(0.3160) Fuel Oil 1998 2001 1 -0.1249

(-1.4777)

0.2476

(0.8851)

0.2517

(0.8952) Motor Petrol 1989 1999 4 -0.3441

(-2.4193)

0.0571

(1.2473)

-0.1014*

(-2.0107) LPG 1998 2006 3 -0.5385

*

(-3.7321)

1.0856***

(3.2248)

-0..2585

(-1.3175) Kerosene 1993 1997 1 -0.2878

(-2.1928)

0.0924

(0.5165)

0.1832

(1.0010) ATF and AV Gas 1985 2000 2 -0.2892

(-2.7196)

0.2734**

(2.4298)

0.2026**

(2.0619) Non-Energy 1991 1997 0 -0.5669

(-3.3280)

0.1739

(1.1335)

-0.2393

(-1.5187) Natural Gas 1988 1991 1 -0.1965

(-2.8226)

0.1712

(0.6251)

0.2335

(0.8729) Coal and Coke 1984 1998 0 -0.2866

(-2.1640)

0.3935

(1.1305)

-0.4525

(-1.3664) Electricity 1984 2000 4 -0.1516

*

(-3.7202)

-0.0451

(-1.9983)

0.0823**

(2.7167) Energy Demand by Sectors

Industrial 1987 1989 1 -0.1815

(-1.9430)

-0.1126

(-1.2764)

-0.0460

(-0.4976) Transport 1989 1997 3 -0.1363

(-1.7155)

0.0682

(1.6622)

-0.1192**

(-2.7301) Non-Energy 1984 2003 0 -0.5342

(-3.1943)

0.4542**

(2.3613)

-0.2731

(-1.4003) Residential and

Commercial 1993 1995 0 -0.6637

*

(-3.7292)

0.0738**

(2.3087)

0.2074***

(5.9870)

Notes: In addition to the notes in Table 4, TB1 and TB2 are the dates of the structural breaks; Bt1 and Bt2

are the dummy variables for the structural breaks in the intercept.

26

Table 7: Results of LM Test with Two Breaks (Lee and Strazicich, [42]) - Model

CC

Series TB1 TB2 k St-1 Bt1 Bt2 Dt1 Dt2

Energy Demand by Fuel Type

Diesel 1999 2004 2 -1.5975***

(-6.6137) 0.4900

**

(2.2212)

-0.2197

(-1.0396)

-0.1586

(-1.6733)

0.0385

(0.2426)

Fuel Oil 1986 1999 4 -1.3370***

(-6.5808) -0.5511

***

(-2.8175)

0.2188

(1.2386)

0.0750

(0.8195) -0.1254

*

(-1.7203)

Motor Petrol 1986 1996 0 -1.0126

(-5.1635)

0.0112

(0.2830)

0.0130

(0.3406) -0.1062

***

(-4.0159)

0.0157

(0.7741)

LPG 1992 2007 0 -1.2394***

(-6.5088) 0.2921

*

(1.9894)

-0.4583**

(-2.6424)

0.1402**

(2.1001)

0.3273***

(3.0739)

Kerosene 1991 2001 4 -1.4879

(-5.0001)

-0.1400

(-0.8994)

-0.0521

(-0.3030)

-0.0578

(-0.8385)

0.1076

(1.3024)

ATF and AV

Gas

1987 1997 2 -1.9413**

(-6.1189) -0.3085

***

(-3.4614)

0.1683**

(2.4542)

0.0237

(0.7295) -0.1749

***

(-5.7230)

Non-Energy 1987 1992 1 -1.1860

(-5.1452)

0.2759

(1.6263)

-0.0006

(-0.0039) -0.4591

***

(-3.5444)

0.2516**

(2.4574)

Natural Gas 1987 2004 4 -2.2970**

(-6.1639) -0.7735

**

(-2.6701)

-0.2475

(-1.4585) 1.1796

***

(4.2397)

0.4940***

(4.1419)

Coal and Coke 1986 1992 4 -2.9089***

(-8.3097) 0.6211

**

(2.3795)

-0.8988***

(-3.6690)

-1.4342***

(-7.1784)

0.0550

(0.5543)

Electricity 1985 1997 4 -0.7713**

(-5.6690)

-0.0042

(-0.2577) -0.0593

***

(-3.0354)

-0.0085

(-0.6360) -0.0234

**

(-2.3050)

Energy Demand by Sectors

Industrial 1992 2000 4 -1.0245

(-5.3077) -0.2343

**

(-2.7230)

-0.0393

(-0.5477)

0.0218

(0.6330) 0.0882

**

(2.1454)

Transport 1985 1999 4 -1.9587

(-5.2136)

0.0652

(1.6988) -0.1191

**

(-2.3882)

-0.0999***

(-3.2562)

0.0368*

(1.7396)

Non-Energy 1993 2001 3 -1.7475*

(-5.5857) -0.4813

**

(-2.5016)

0.1019

(0.5856)

0.0035

(0.0443) -0.1922

**

(-2.1345)

Residential and

Commercial

1991 1997 0 -1.0921*

(-5.5925)

-0.0314

(-0.9121)

0.0018

(0.0548) 0.0458

**

(2.7546)

-0.0516***

(-3.0689)

Critical values for the LM test

λ2 0.4 0.6 0.8

λ1 1% 5% 10% 1% 5% 10% 1% 5% 10%

0.2 -6.16 -5.59 -5.27 -6.41 -5.74 -5.32 -6.33 -5.71 -5.33 0.4 - - - -6.45 -5.67 -5.31 -6.42 -5.65 -5.32 0.6 - - - - - - -6.32 -5.73 -5.32

Notes: In addition to the notes in Tables 4 and 6, Dt1 and Dt2 are the dummy variables for the structural

breaks in the slope. λj denotes the location of breaks.

27

Table 8: Preferred Results Based on Comparing the No-break Case with

Models C and CC

Series Unit root null rejected? Rule of thumb

requiring break in

the intercept and

slope

Rule of thumb

requiring break in

the intercept or

slope

Diesel No No

Fuel Oil No Yes

Motor Petrol Yes Yes

LPG Yes Yes

Kerosene No No

ATF and AV Gas Yes Yes

Non-Energy No No

Natural Gas No Yes

Coal and Coke Yes Yes

Electricity Yes Yes

Industrial No No

Transport No No

Non-Energy Yes Yes (10 per cent)

Residential and

Commercial

No Yes (10 per cent)

28

Figure 1: Time Series Plot for Final Energy Demand by Fuel Type

0

2000

4000

6000

8000

10000

78 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10

Ktoe

Year

Diesel

0

500

1000

1500

2000

2500

78 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10

Ktoe

Year

Fuel Oil

0

2000

4000

6000

8000

10000

12000

78 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10

Ktoe

Year

Motor Petrol

29

0

500

1000

1500

2000

2500

3000

3500

78 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10

Ktoe

Year

LPG

050

100150200250300350400

78 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10

Ktoe

Year

Kerosene

0

500

1000

1500

2000

2500

78 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10

Ktoe

Year

ATF and AV Gas

30

0

200

400

600

800

1000

78 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10

Ktoe

Year

Non-Energy

0

2000

4000

6000

8000

10000

12000

78 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10

Ktoe

Year

Natural Gas

0

500

1000

1500

2000

78 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10

Ktoe

Year

Coal and Coke

31

0

2000

4000

6000

8000

10000

78 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10

Ktoe

Year

Electricity

32

Figure 2: Time Series Plots for Final Energy Demand by Sectors

0

5000

10000

15000

20000

25000

78 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10

Ktoe

Year

Industrial

02000400060008000

1000012000140001600018000

78 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10

Ktoe

Year

Transport

0500

10001500200025003000350040004500

78 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10

Ktoe

Year

Non-Energy

33

010002000300040005000600070008000

78 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10

Ktoe

Year

Residential and Commercial